1.1.  Enhancing Interoperability for Climate Resilience Information Systems

The OGC Climate Resilience Pilot will be the first phase of multiple long term climate activities aiming to evolve geospatial data, technologies, and other capabilities into valuable information for decision makers, scientists, policy makers, data providers, software developers, and service providers so we can make valuable, informed decisions to improve climate action. The goal is to help the location community develop more powerful visualization and communication tools to accurately address ongoing climate threats such as heat, drought, floods, fires as well as supporting the national determined contributions for greenhouse gas emission reduction. Climate resilience is often considered the use case of our lifetime, and the OGC community is uniquely positioned to accelerate solutions through collective problem solving with this initiative.

Figure 1

As illustrated, big, raw data from multiple sources requires further processing in order to be ready for analysis and climate change impact assessments. Applying data enhancement steps, such as bias adjustments, re-gridding, or calculation of climate indicators and essential variables, leads to “Decision Ready Indicators.” The spatial data infrastructures required for this integration should be designed with interoperable building blocks following FAIR data principles. Heterogeneous data from multiple sources can be enhanced, adjusted, refined, or quality controlled to provide Science Services data products for Climate Resilience. The OGC Climate Change Services Pilots will also illustrate the graphical exploration of the Decision Ready Climate Data. It will demonstrate how to design FAIR climate services information systems. The OGC Pilot demonstrators will illustrate the necessary tools and the visualizations to address climate actions moving towards climate resilience.

1.2.  The Role of the Pilot

The OGC Climate Resilience Community brings decision makers, scientists, policy makers, data providers, software developers, and service providers together. The goal is to enable everyone to take the relevant actions to address climate change and make well informed decisions for climate change adaptation. This includes scientists, decision makers, city managers, politicians, and last but not least, it includes everyone of us. So what do we need? We need data from lots of organizations, available at different scales for large and small areas to be integrated with scientific processes, analytical models, and simulation environments. We need data visualization and communication tools to shape the message in the right way for any client. Many challenges can be met through resources that adhere to FAIR principles. FAIR as in: Findable, Accessible, Interoperable, and Reusable. No single organization has all the data we need to understand the consequences of climate change. The OGC Climate Resilience Community identifies, discusses, and develops these resources. The OGC community builds the guidebooks and Best Practices, it experiments with new technologies to share data and information, and collaboratively addresses shared challenges.

The OGC Climate Resilience Community has a vision to support efforts on climate actions and enable international partnerships (SDG 17), and move towards global interoperable open digital infrastructures providing climate resilience information on users demand. This pilot will contribute to establishing an OGC climate resilience concept store for the community where all appropriate climate information to build climate resilience information systems as open infrastructures can be found in one place, be it Information about data services, tools, software, handbooks, or a place to discuss experiences and needs. The concept store covers all phases of Climate Resilience, from initial hazards identification and mapping to vulnerability and risk analysis to options assessments, prioritization, and planning, and ends with implementation planning and monitoring capabilities. These major challenges can only be met through the combined efforts of many OGC members across government, industry, and academia.

This Call for Participation solicits interests from organizations to join the upcoming Climate Resilience Pilot, an OGC Collaborative Solution and Innovation Program activity. This six-months Pilot is setting the stage for a series of follow up activities. It therefore focuses on use-case development, implementation, and exploration. It answers questions such as:

• What use-cases can be realized with the current data, services, analytical functions, and visualization capabilities that we have?

• How much effort is it to realize these use-cases?

• What is missing, or needs to be improved, in order to transfer the use-cases developed in the pilot to other areas?

1.3.  Objectives

The pilot has three objectives. First, to better understand what is currently possible with the available data and technology. Second, what additional data and technology needs to be developed in future to better meet the needs of the Climate Resilience Community; and third, to capture Best Practices, and to allow the Climate Community to copy and transform as many use-cases as possible to other locations or framework conditions.

1.4.  Background

With growing local communities, an increase in climate-driven disasters, and an increasing risk of future natural hazards, the demand for National Resilience Frameworks and Climate Resilience Information Systems (CRIS) cannot be overstated. Climate Resilience Information Systems (CRIS) are enabling data-search, -fetch, -fusion, -processing and -visualization. They enable access, understanding, and use of federal data, facilitate integration of federal and state data with local data, and serve as local information hubs for climate resilience knowledge sharing.

CRIS are already existing and operational, like the Copernicus Climate Change Service with the Climate Data Store. CRIS architectures can be further enhanced by providing climate scientific methods and visualization capabilities as climate building blocks. Based on FAIR principles, these building blocks enable in particular the reusability of Climate Resilience Information Systems features and capabilities. Reusability is an essential component when goals, expertises, and resources are aligned from the national to the local level. Framework conditions differ across the country, but building blocks enable as much reuse of existing Best Practices, tools, data, and services as possible.

Goals and objectives of decision makers vary at different scales. At the municipal level, municipal leaders and citizens directly face climate-related hazards. Aspects thus come into focus such as reducing vulnerability and risk, building resilience through local measures, or enhancing emergency response. At the state level, the municipal efforts can be coordinated and supported by providing funding and enacting relevant policies. The national, federal, or international level provides funding, science data, and international coordination to enable the best analysis and decisions at the lower scales.

Figure 2

Productivity and decision making are enhanced when climate building blocks are exchangeable across countries, organizations, or administrative levels (see Figure below). This OGC Climate Resilience Pilot is a contribution towards an open, multi-level infrastructure that integrates data spaces, open science, and local-to-international requirements and objectives. It contributes to the technology and governance stack that enables the integration of data including historical observations, real time sensing data, reanalyses, forecasts or future projections. It addresses data-to-decision pipelines, data analysis and representation, and bundles everything in climate resilience building blocks. These building blocks are complemented by Best Practices, guidelines, and cook-books that enable multi–stakeholder decision making for the good of society in a changing natural environment.

The OGC Innovation Program brings all groups together: The various members of the stakeholder group define use cases and requirements, the technologists and data providers experiment with new tools and data products in an agile development process. The scientific community provides results in appropriate formats and enables open science by providing applications that can be parameterized and executed on demand.

Figure 3

This OGC Climate Resilience Pilot is part of the OGC Climate Community Collaborative Solution and Innovation process, an open community process that uses the OGC as the governing body for collaborative activities among all members. A spiral approach is applied to connect technology enhancements, new data products, and scientific research with community needs and framework conditions at different scales. The spiral approach defines real world use cases, identifies gaps, produces new technology and data, and tests these against the real world use cases before entering the next iteration. Evaluation and validation cycles alternate and continuously define new work tasks. These tasks include documentation and toolbox descriptions on the consumer side, and data and service offerings, interoperability, and system architecture developments on the producer side. It is emphasized that research and development is not constrained to the data provider or infrastructure side. Many tasks need to be executed on the data consumer side in parallel and then merged with advancements on the provider side in regular intervals.

Good experiences have been made using OGC API standards in the past. For example, the remote operations on climate simulations (roocs) use OGC API Processes for subsetting data sets to reduce the data volume being transported. Other systems use OGC STAC for metadata and data handling or OGC Earth Observation Exploitation Platform Best Practices for the deployment of climate building blocks or applications into CRIS architectures. Still data handling regarding higher complex climate impact assessments within FAIR and open infrastructures needs to be enhanced. There is no international recommendation or best practice on usage of existing API standards within individual CRIS. It is the goal of this pilot to contribute to the development of such a recommendation, respecting existing operational CRIS that are serving heterogen user groups

Figure 4

1.5.  Climate Indices

To make planning decisions to build resilience and adapt to future climate, government officials from local to national, as well as corporate leaders and citizens need an approachable yet scientifically rigorous view of their local climate. We propose a dynamic web mapping interface and report generation tool backed by a suite of web services and downloadable data. All data and web service deliverables will be provided following FAIR principles at no cost, using the appropriate OGC standards.

The climate indices describe 47 measures of future temperature and precipitation in 3 future time periods (early, mid, late century) under 2 emission scenarios RCP 4.5 and 8.5. These indices were created to inform understanding of 5 climate hazards (Wildfire, Heat, Drought, Inland Flooding, Coastal Inundation). Wildfire and drought are the current focus of the disaster pilot and these climate indices will provide useful in those projects when considering future climate.

The project will present a pattern with reproducible workflows in an open Github repo showing the full process of transforming climate science data (CMIP model outputs) into a collection of analysis ready data layers (47 temperature and precipitation indices) and transforming those into decision ready information as climate indices summarized to local geographies such as counties and other subnational boundaries.

1.6.  Technical Challenges

Realizing the delivery of Decision Ready Data on demand to achieve Climate Resilience involves a number of technical challenges that have already been identified by the community. A subset will be selected and embedded in use-cases that will be defined jointly by Pilot Sponsors and the OGC team. The goal is to ensure a clear value-enhancement pipeline as illustrated in Figure 1, above. This includes, among other elements, a baseline of standardised operators for data reduction and analytics. These need to fit into an overall workflow that provides translation services between upstream model data and downstream output — basically from raw data, to analysis-ready data, to decision-ready data. The following technical challenges have been identified and will be treated in the focus areas cycles of the Pilot accordingly:

• Big Data Challenge: Multiple obstacles still exist, creating big barriers for seamless information delivery starting from Data Discovery. Here the emergence of new data platforms, new processing functionalities, and thus new products, data discovery remains a challenge. In addition to existing solutions based on established metadata profiles and catalog services, new technologies such as OGC’s Spatio-Temporal Asset Catalog (STAC) and open Web APIs such as OGC API Records will be explored. Furthermore, aspects of Data Access need to be solved where the new OGC API suite of Web APIs for data access, subsetting, and processing are currently utilized very successfully in several domains. Several code sprints have shown that server-side solutions can be realized within days and clients can interact very quickly with these server endpoints, thus development time is radically reduced. A promising specialized candidate for climate data and non-climate data integration has been recently published in the form of the OGC API — Environmental Data Retrieval (EDR). But which additional APIs are needed for climate data? Is the current set of OGC APIs sufficiently qualified to support the data enhancement pipeline illustrated in Figure 1? If not, what modifications and extensions need to be made available? How do OGC APIs cooperate with existing technologies such as THREDDS and OPEnDAP? For challenges of data spaces, Data Cubes have recently been explored in the OGC data cube workshop. Ad hoc creation and embedded processing functions have been identified as essential ingredients for efficient data exploration and exchange. Is it possible to transfer these concepts to all stages of the processing pipeline? How to scale both ways from local, ad hoc cubes to pan-continental cubes and vice versa. How to extend cubes as part of data fusion and data integration processes?

• Cross-Discipline Data Integration: Different disciplines such as Earth Observation, various social science, or climate modeling use different conceptual models in their data collection, production, and analytical processes. How can we map between these different models? What patterns have been used to transform conceptual models to logical models, and eventually physical models? The production of modern Decision-ready information needs the integration of several data sets, including census and demographics, further social science data, transportation infrastructure, hydrography, land use, topography and other data sets. This pilot cycle uses ‘location’ as the common denominator between these diverse data sets and works with several data providers and scientific disciplines. In terms of Data Exchange Formats the challenge is to know what data formats need to be supported at the various interfaces of the processing pipeline? What is the minimum constellation of required formats to cover the majority of use cases? What role do container formats play? Challenging on technical level is also the Data Provenance. Many archives include data from several production cycles, such as IPCC AR 5 and AR 6 models. In this context, long term support needs to be realized and full traceability from high level data products back to the original raw data. Especially in context of reliable data based policy, clear audit trails and accountability for the data to information evolution needs to be ensured.

• Building Blocks for processing pipelines: With a focus on Machine Learning and Artificial Intelligence which plays an increasing role in the context of data science and data integration. This focus area needs to evaluate the applicability of machine learning models in the context of the value-enhancing processing pipeline. What information needs to be provided to describe machine learning models and corresponding training data sufficiently to ensure proper usage at various steps of the pipeline? Upcoming options to deploy ML/AI within processing APIs to enhance climate services are rising challenges e.g. on how to initiate or ingest training models and the appropriate learning extensions for the production phase of ML/AI. Heterogeneity in data spaces can be bridged with Linked Data and Data Semantics. Proper and common use of shared semantics is essential to guarantee solid value-enhancement processes. At the same time, resolvable links to procedures, sampling & data process protocols, and used applications will ensure transparency and traceability of decisions and actions based on data products. What level is currently supported? What infrastructure is required to support shared semantics? What governance mechanisms need to be put in place?

1.7.  How is this Pilot Relevant to the Climate Resilience Domain Working Group?

The Climate Resilience DWG will concern itself with technology and technology policy issues, focusing on geospatial information and technology interests as related to climate mitigation and adaptation as well as the means by which those issues can be appropriately factored into the OGC standards development process.

The mission of the Climate Resilience DWG is to identify geospatial interoperability issues and challenges that impede climate action, then examine ways in which those challenges can be met through application of existing OGC Standards, or through development of new geospatial interoperability standards under the auspices of OGC.

Activities to be undertaken by the Climate Resilience DWG include but are not limited to:

• Identify the OGC interface standards and encodings useful to apply FAIR concepts to climate change services platforms;

• Liaise with other OGC Working Groups (WGs) to drive standards evolution;

• Promote the usage of the aforementioned standards with climate change service providers and policy makers addressing international regional and local needs;

• Liaise with external groups working on technologies relevant to establishing ecosystems of EO Exploitation Platforms;

• Liaise with external groups working on relevant technologies;

• Publish OGC Technical Papers, Discussion Papers or Best Practices on interoperable interfaces for climate change services;

• Provide software toolkits to facilitate the deployment of climate change services platforms.

2.1.  About Laubwerk

Laubwerk is a software development company whose mission is to combine accurate, broadly applicable visualizations of vegetation with deeper information and utility that goes far beyond their visual appearance. We achieve this through building a database that combines ultra-realisting 3D representation of plants with extensive metadata that represents plant properties. This unique combination makes Laubwerk a prime partner to bridge the gap from data-driven simulation to eye-catching visualizations.

2.2.  About Pixalytics Ltd

Pixalytics Ltd is an independent consultancy company specializing in Earth Observation (EO). We combine cutting-edge scientific knowledge with satellite and airborne data to provide answers to questions about our planet’s resources and behavior. The underlying work includes developing algorithms and software, with activities including a focus on EO quality control and end-user focused applications.

2.3.  About Safe Software

Safe Software is a leader in supporting geospatial interoperability and automation for more than 25 years as creators of the FME platform. FME was created to promote FAIR principles, including data sharing across barriers and silos, with unparalleled support for a wide array of both vendor specific formats and open standards. Within this platform, Safe Software provides a range of tools to support interoperability workflows. FME Form is a graphical authoring environment that allows users to rapidly prototype transformation workflows in a no-code environment. FME Flow then allows users to publish data transforms to enterprise oriented service architectures. FME Hosted offers a low cost, easy to deploy and scalable environment for deploying transformation and integration services to the cloud.

Open standards have always been a core strategy for Safe to better support data sharing. The FME platform can be seen as a bridge between the many supported vendor protocols and open standards such as XML, JSON and OGC standards such as GML, KML, WMS, WFS and OGC APIs. Safe has collaborated extensively over the years with the open standards community. Safe actively participates in the CityGML and INSPIRE communities in Europe. We are also active within the OGC community and participated in many initiatives including test beds, pilots such as Maritime Limits and Boundaries and IndoorGML, and most recently the 2021 Disaster Pilot and 2023 Climate Resilience Pilot. Safe also actively participates in a number of Domain and Standards working groups.

2.4.  About Intact

Intact Financial Corporation (IFC) is the largest provider of Property & Casualty (P&C) insurance in Canada. Its purpose is to help people, businesses and society prosper in good times and be resilient in bad times [1]. The company has been on the front lines of climate change with our customers for more than a decade – getting them back on track and helping them adapt. As extreme weather is going to get worse over the next decade, Intact intends to double down on adapting to this changing environment and be better prepared for floods, wildfire and extreme heat [2].

With close to 500 experts in data, artificial intelligence, machine learning and pricing, the Intact Data Lab has deployed almost 300 AI models in production to-date. It is focused on improving risk selection and making operations as efficient as possible while creating outstanding interactions with customers. Within Intact’s Data Lab, the Centre for Climate and Geospatial Analytics (CCGA) uses weather, climate, and geospatial data along with machine learning models and claims data to develop risk maps and other specialized products to the business.

2.5.  About Pelagis

Pelagis is an OceanTech venture located in Nova Scotia, Canada. Our foundation focuses on the application of open geospatial technology and standards designed to promote the sustainable use of our ocean resources. As a member of the Open Geospatial Consortium, we co-chair the Marine Domain Working Group responsible for developing a spatially-aware federated service model of marine and coastal ecosystems.

3.1.  Component workflow

The figure below shows a high level workflow diagram that illustrates the interactions between data, models and the various components.

Figure 6 — High level workflow diagram that illustrates the interactions between data, models and the various components

4.1.  Using Datacubes for wildfire risk analysis

Ecere is providing a deployment of its GNOSIS Map Server with a focus on a Sentinel-2 Level 2A data cube. OGC API - Tiles, OGC API - Coverages, OGC API - Maps, OGC API — Discrete Global Grid Systems, Common Query Language (CQL2), and OGC API — Processes — Part 3: Workflows & Chaining are the supported standards and extensions for this task.

The plan is to use machine learning process output from the Wildland Fire Fuel Indicator Workflow to identify vegetation fuel types from sentinel-2 bands, then combine with weather data to assess wildfire hazards risk in Australia. The workflow will use as input the sentinel-2 OGC API data cube from our GNOSIS Map Server.

• Component: Data Cube and Wildfire vegetation fuel map / risk analysis.

• Inputs: ESA Sentinel-2 L2A data (from AWS / Element 84), Temperature / Precipitation / Wind climate data, Reference data for training: vegetation fuel type classification, wildfire risk.

The sentinel-2 Level 2A collection is provided at https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a

• Outputs: OGC API (Coverage, Tiles, DGGS, Maps) for Sentinel-2 data (https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a) including full global coverage, all resolutions/scales, all bands that can be individually selected, CQL2 expressions for band arithmetics; climate data (to be added), vegetation fuel type (possibly by end of pilot, or for DP2023), wildfire risk workflow (possibly by end of pilot, or for DP2023).

• What other component(s) can interact with the component: Any OGC API client component requiring efficient access to Sentinel-2 data, clients requiring climate data once made available, clients presenting vegetation fuel type, wildfire risk (once ready, might extend into DP2023).

• What OGC standards or formats does the component use and produce:

  • OGC API (Coverage — with subsetting, scaling, range subsetting, coverage tiles; Tiles, DGGS (GNOSISGlobalGrid and ISEA9R), Maps (incl. map tiles), Styles), CQL2, OGC API — Processes with Part 3 for workflows (Nested Local/Remote Processes, Local/Remote Collection Input, Collection Output, Input/Output Field Modifiers)

  • Formats: GNOSIS Map Tiles (Gridded Coverage, Vector Features, Map imagery, and more); GeoTIFF; PNG (16-bit value single channel for coverage, RGBA for maps); JPEG.

4.1.1.  Overview of standards and extensions available for outputs

4.1.1.1.  OGC API — DGGS

There are two main requirements classes for this standard.

• Data Retrieval (What is here? — ”give me the data for this zone”),

• Zones Query (Where is it? — ”which zones match this collection and/or my query”)

Example of data retrieval queries:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/GNOSISGlobalGrid/zones/3-4-11/data
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/ISEA9Diamonds/zones/E7-FAE/data

Figure 7

Example of a zones query:

https://maps.gnosis.earth/ogcapi/collections/SRTM_ViewFinderPanorama/dggs/ISEA9Diamonds/zones
https://maps.gnosis.earth/ogcapi/collections/SRTM_ViewFinderPanorama/dggs/ISEA9Diamonds/zones?f=json (as a list of compact JSON IDs)

Figure 8

Level, Row, Column (which encoded differently in the compact hexadecimal zone IDs) can be seen on the zone information page at:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/GNOSISGlobalGrid/zones/3-4-11
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/dggs/ISEA9Diamonds/zones/E7-FAE

Figure 9

There are several different discrete global grids. Two are implemented in our service:

• Our GNOSIS Global Grid, which is geographic rather than projected, and is axis-aligned with latitudes and longitudes, but not equal area (though it tends towards equal area — maximum variation is ~48% up to a very detailed level)

• ISEA9R, which is a dual DGGS of ISEA3H even levels, using rhombuses/diamonds instead of hexagons, but much simpler to work with and can transport the hexagon area values as points on the rhombus vertices for those ISEA3H even levels. It is also axis-aligned to a CRS defined by rotating and skewing the ISEA projection.

The primary advantage of OGC API — DGGS is:

• for retrieving data from DGGS that are not axis-aligned or have geometry that cannot be represented as squares (e.g., hexagons), or

• for the zone query capability, most useful for specifying queries (e.g. using CQL2). The extent to which we implement Zones Query at this moment is still limited.

Examples of DGGS Zone information page:

Figure 10 — GNOSIS Map Server information resource for GNOSIS Global Grid zone 5-24-6E

Figure 11 — GNOSIS Map Server information resource for ISEA9Diamonds zone 5-24-6E

Figure 12 — GNOSIS Map Server information resource for ISEA9Diamonds zone 5-24-6E sections

4.1.1.2.  OGC API — Coverages with OGC API — Tiles

Because they are axis-aligned, both of these DGGS can be described as a TileMatrixSet, and therefore equivalent functionality to the OGC API — DGGS Data Retrieval requirements class can be achieved using OGC API — Tiles and the corresponding TileMatrixSets instead.

Coverage Tile queries for the same zones:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/GNOSISGlobalGrid/3/4/17
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/ISEA9Diamonds/4/373/288

Figure 13

To request a different band than the default RGB (B04, B03, B02) bands:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/GNOSISGlobalGrid/3/4/17?properties=B08
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/ISEA9Diamonds/4/373/288?properties=B08

Figure 14

To retrieve coverage tiles with band arithmetic to compute NDVI:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/GNOSISGlobalGrid/3/4/17?properties=(B08/10000-B04/10000)/(B08/10000+B04/10000)
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/coverage/tiles/ISEA9Diamonds/4/373/288?properties=(B08/10000-B04/10000)/(B08/10000+B04/10000)

Figure 15

4.1.1.3.  OGC API — Maps with OGC API — Tiles

Map Tiles queries for the same zones:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/GNOSISGlobalGrid/3/4/17
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/ISEA9Diamonds/4/373/288

Figure 16

Figure 17 — GNOSIS Map Server Map of tiles 3/4/17 in GNOSISGlobalGrid

To retrieve a map of the Scene Classification:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/styles/scl/map/tiles/GNOSISGlobalGrid/3/4/17
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/styles/scl/map/tiles/ISEA9Diamonds/4/373/288

Figure 18

Figure 19 — Sentinel-2 with image classification styling

To filter out the clouds:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/GNOSISGlobalGrid/3/4/17?filter=SCL<8 or SCL >10
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/map/tiles/ISEA9Diamonds/4/373/288?filter=SCL<8 or SCL >10

Figure 20

To get an NDVI map:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/styles/ndvi/map/tiles/GNOSISGlobalGrid/3/4/17
https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a/styles/ndvi/map/tiles/ISEA9Diamonds/4/373/288

Figure 21

Figure 22 — Sentinel-2 map with NDVI band arithmetic

The same filter= and properties= should also work with the /coverage and /dggs end-points. The filter= also works with the /map end-points.

4.1.2.  GNOSIS implementation of OGC API for climate data cube (2016-2025 CMIP5 data)

There is now a fairly complete set of variables from the CMIP5 global dataset (from the Copernicus Climate Data Store) for the 2016-2025 time period available from our GNOSIS data cube implementation at: https://maps.gnosis.earth/ogcapi/collections/climate:cmip5

The variables on a single pressure level are organized as a single collection (coverage / data cube) at: https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure (consisting of 9 fields: specific humidity, precipitation, snowfall, sea level pressure, downwelling shortwave radiation, wind speed, mean surface air temperature, maximum daily air temperature, minimum daily air temperature), while the variables on multiple pressure levels are organized into three separate collections: https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:temperature https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:gpHeight https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:windSpeed (consisting of two separate fields for Eastward and Northward wind velocity)

The temporal resolution of this dataset is daily, while the source spatial resolution is 2.5 degrees longitude x 2 degrees of latitude, and it is for 8 different pressure levels. Currently, the API supports requesting data from this data using OGC API — Tiles (coverage tiles as well as map tiles), Coverages, Maps and DGGS. With all these APIs, a specific pressure level can be specified for the multi-pressure using e.g., subset=pressure(500), while a specific time can be requested using e.g., datetime=2022-03-01 or subset=time(“2022-03-01”). With Coverages and Maps, a spatial area of interest can be specified using either e.g., bbox=10,20,30,40 or subset=Lat(20:40),Lon(10:30).

At the moment, the Coverages API is limited to 2D output formats (spatial trim, slicing by time and pressure): GeoTIFF and PNG (16-bit output, currently fixed scale: 2.98 and offset: 16384). There is a plan to add support for n-dimensional output formats, including netCDF, CIS JSON and eventually CoverageJSON as well. Currently, separate API requests with the above parameters are needed for different times/pressure levels.

For coverage output, the fields can be selected using properties= (a single field for PNG, and one or more fields for GeoTIFF) e.g., properties=tasmin,tasmax The fields can also be derived using CQL2 expressions that can perform arithmetic e.g., properties=pr*1000.

With all these APIs, it is also possible to filter fields with filter= also specified as a CQL2 expression e.g., filter=tasmax>300 (unmatched cells will be replaced by NODATA values). The domains of the collections are described in the collection description (inside the extent property) as well as in the Coverages CIS DomainSet resource e.g., https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure?f=json , https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage/domainset?f=json

The ranges of the collections are described in the Coverages CIS RangeType resource as per the example below, and we are also planning to implement describing in a /schema resource that will be harmonized with the OGC API — Features schema. https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage/rangetype?f=json

Some sample requests: Maps

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/styles/precipitation/map?datetime=2022-09-04

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:windSpeed/map?subset=pressure(850)=1024

Proper symbolization here will require support for wind barbs — in the meantime the Eastward and Northward velocity are assigned to the green and blue color channels.

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:byPressureLevel:temperature/map?subset=pressure(850)

Tiles

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/styles/precipitation/map/tiles/WebMercatorQuad/1/1/0?datetime=2022-09-04

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage/tiles/WebMercatorQuad/1/1/0?f=geotiff=2022-09-04 (GeoTIFF Coverage Tile)

DGGS

Data retrieval — What is here? (equivalent to Coverage Tiles requests for DGGSs whose zone geometry can be described by a 2D Tile Matrix Set e.g., GNOSISGlobalGrid, ISEA9R, rHealPix):

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/dggs/GNOSISGlobalGrid/zones/0-0-3/data?f=geotiff=2022-09-04

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/dggs/ISEA9Diamonds/zones/A7-0/data?f=geotiff=2022-09-04

Zones query — Where is it?: Where is maximum daily temperature greater than 300 degrees Kelvins on September 4, 2022? (at precision level of GNOSIS Global Grid level 6)

Figure 23 — GeoJSON output

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/dggs/GNOSISGlobalGrid/zones?filter=tasmax%3E300=2022-09-04=6=json

(Plain JSON Zone ID list output)

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/dggs/GNOSISGlobalGrid/zones?filter=tasmax%3E300=2022-09-04=6=uint64

(Binary 64-bit integer Zone IDs)

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/dggs/GNOSISGlobalGrid/zones?filter=tasmax%3E300=2022-09-04=6=geotiff

(GeoTIFF output) (using the default compact-zones=true where children zones are replaced by parent zone if all children zones are included)

By creating a kind of mask at a specifically requested resolution level, DGGS Zones Query can potentially greatly help parallelization and orchestration of spatial queries combining multiple datasets across multiple services, allowing to perform early optimizations with lazy evaluation.

Coverages

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage?f=png=(tasmax-250)*400

https://maps.gnosis.earth/ogcapi/collections/climate:cmip5:singlePressure/coverage?f=geotiff=tas,tasmax,tasmin,pr,psl=Lat(-90:90),Lon(0:180)=400=2020-05-20

(GeoTIFF coverage with 5 bands for each field)

As a test of higher resolution data, we also loaded an hourly dataset for the ERA5 relative humidity for the April 1-6, 2023 period at: https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity

The spatial resolution for this one is also higher at 0.25 degrees longitude x 0.25 degrees latitude, and the data is for 37 different pressure levels. Some sample requests:

Maps

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/map?width=2048=pressure(750)=0x002040

Tiles

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/map/tiles/WorldCRS84Quad/0/0/0?subset=pressure(750)=0x002040

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/coverage/tiles/WorldCRS84Quad/0/0/0?f=geotiff=pressure(750) (GeoTIFF coverage tile)

Coverages

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/coverage?f=png=pressure(750),Lat(-90:90),Lon(0:180),time(%222023-04-03%22)=r*200=r%3E20

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/coverage?f=geotiff=pressure(750),Lat(-90:90),Lon(0:180),time(%222023-04-03%22)

(GeoTIFF Coverage)

DGGS

Data retrieval — What is here? (equivalent to Coverage Tiles requests for DGGSs whose zone geometry can be described by a 2D Tile Matrix Set e.g., GNOSISGlobalGrid, ISEA9R, rHealPix):

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/GNOSISGlobalGrid/zones/0-0-3/data?f=geotiff=2023-04-03

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/ISEA9Diamonds/zones/A7-0/data?f=geotiff=2023-04-03

Zones query — Where is it?: Where is relative humidity at 850 hPa greater than 80% on April 3rd, 2023? (at precision level of GNOSIS Global Grid level 6) https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/GNOSISGlobalGrid/zones?subset=pressure(850)=2023-04-03=r%3E80=6=geojson

Figure 24 — GeoJSON output

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/GNOSISGlobalGrid/zones?subset=pressure(850)=2023-04-03=r%3E80=6=json

(Plain Zone ID list output)

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/GNOSISGlobalGrid/zones?subset=pressure(850)=2023-04-03=r%3E80=6=uint64

(Binary 64-bit integer Zone IDs)

https://maps.gnosis.earth/ogcapi/collections/climate:era5:relativeHumidity/dggs/GNOSISGlobalGrid/zones?subset=pressure(850)=2023-04-03=r%3E80=6=geotiff

(GeoTIFF output) (using the default compact-zones=true where children zones are replaced by parent zone if all children zones are included)

We hope that our API and these climate datasets proves useful to other participants and can be part of Technology Integration Experiments for the pilots and/or Testbed 19 GeoDataCube.

We have also been working on our client to visualize these data sources from local netCDF files, our native GNOSIS data store, or remotely through OGC APIs, and we are working on support for EDR in order to perform integration experiments with the NOAA EDR API.

Figure 25 — GeoJSON output

We are also planning work on demonstrating the integration of these datasets as cross-collection queries and with our OGC API — Processes implementation including support for Part 3 — Workflows and Chaining.

One process we are putting together is a machine learning prediction process for classifying fuel vegetation types, based on sentinel-2 Level 2A accessed through our API at:

https://maps.gnosis.earth/ogcapi/collections/sentinel2-l2a

The initial training data will be using this Fuel vegetation Type coverage for the whole continental US from landfire.gov available from our API at:

https://maps.gnosis.earth/ogcapi/collections/wildfire:USFuelVegetationTypes

More work is being done on loading additional fire danger indices from the Copernicus Climate Data Store.

5.1.  Transforming climate relevant raw data to ARD

Several past successful OGC testbeds, including the DP 21 to which this pilot is linked, have looked at ARD and IRD but also in terms of use cases. In this pilot, some main technical contributions have been creating digestible OGC data types and formats for specific partner use cases, so producing ARD from publically available EO and model data, including hydrological and other type of model output as well as climate projections.

These ARD will feed into all use cases for all participants, with a particular focus toward the use cases proposed for Heat, Drought and Health Impacts by participants in the pilot.

Specifically, participants provide access to the following satellite and climate projection data:

• Wildfire: Fire Radiant Power (FRP) product from Sentinel 3 (NetCDF), 5p, MODIS products (fire detection), VIIRS (NOAA); possibly biomass availability (fire fuel).

• Land Surface Temp — Sentinel 3

• Pollution — Sentinel 5p

• Climate Projection data (NetCDF, etc., daily downscaled possible): air temp (10 m above ground). Rainfall and possibly wind direction as well

• Satellite-derived Discharge Data to look at Droughts/Floods etc. by basin or other scale

• Hydrological model simulation outputs at (sub)basin scale (within reason)

The created ARD in various OGC interoperable formats created digestible dataflows for the proposed OGC Use Cases. This proposed data chain by several participants is similar to DP21, in which contributors, like RSS-Hydro, SafeSoftware, and others also participated. A generated climate indicator or EO remotely sensed data (NASA, NOAA, ESA, etc.) from various sources are “simplified”to GeoTIFF and / or vectorized geopackage per time step by other participants’ tools, such as the FME software (by SafeSoftware). Another option as an intermediate data type (IRD) can be COG — cloud optimized geotiff which would make access more efficient. The COG GeoTIFFs are optimized for cloud so data sharing can be made more efficient. ARD and IRD should become more service / cloud based wherever possible.

Besides the data format, data structures and semantics required to support the desired DRI’s are important. The time series / raster, and classification to vector contour transform is an approach that worked well in DP21 and has been a good starting point also in this pilot. For example, together in the FME processing engine, time series grids can be aggregated across timesteps to mean or max values, then classify them into ranges suitable for decision making, and then write them out and expose them as time tagged vector contour tables.

In summary, the different ARD and IRD data can be created from the following data sources:

• Inputs: EO (US sources fire related: MODIS, VIIRS); Climate projections, sub catchment polygons, ESA sources; Sentinel-3, Sentinel 5-P.

• Outputs forma & instances: WCS, GeoTIFF spatial / temporal subset, Shape; NetCDF.

• Output parameters: e.g. hydrological condition of a basin (historically/current). So drought / flood etc.

• Output themes: downscaled / subset outputs, hydrologic scenarios.

Another highly relevant input are the Essential Climate Variables (ECV) Inventory (https://climatemonitoring.info/ecvinventory/) houses information on Climate Data Records (CDR) provided mostly by CEOS and CGMS member agencies. The inventory is a structured repository for the characteristics of two types of GCOS ECV CDRs:

Climate data records that exist and are accessible, including frequently updated interim CDRs;
Climate data records that are planned to be delivered.

The ECV Inventory is an open resource to explore existing and planned data records from space agency sponsored activities and provides a unique source of information on CDRs available internationally. Access links to the data are provided within the inventory, alongside details of the data’s provenance, integrity and application to climate monitoring.

Participants, particularly GMU CSISS have demonstrated the use of ECV record information as input with OpenSearch service endpoint (currently CMR(CWIC) and FedEO), and downloading URLs for accessing NetCDF or HDF files.

Outputs in this case include WCS service endpoint for accessing selected granule level product images (GeoTIFF, PNG, JPEG, etc.), focusing on the WCS for downloading images and WMS for showing layers on a basemap.

5.2.  Use of API services for climate related use cases

Pixalytics have developed an OGC-compliant Application Programming Interface (API) service, see Figure 27, which will provide global information on droughts. The approach is to take global open data/datasets from organizations such as ESA/Copernicus, NASA/NOAA, and the WMO and combine meteorology, hydrology, and remote sensing data to produce ARD data based on a composite of different indicators. Where globally calculated drought indicators already exist, these are being used in preference to their re-calculation, although consistency and the presence of uncertainties are also being considered.

Figure 27 — Pixalytics drought severity workflow architecture

The Drought Severity Workflow (DSW) is built using individual drought indicators for precipitation (SPI), soil moisture (SMA), and vegetation drought that are together using the Combined Drought Indicator (CDI) as described by [Sepulcre-Canto 2012]. The API access has been set up following the Building Blocks for Climate Services (https://climateintelligence.github.io/smartduck-docs/) approach.

• Component: D100 Drought indicator.

• Inputs: Meteorological data, including Precipitation, plus Land Surface Temperature, Soil Moisture, and Vegetation Index (or optical data to calculate it from).

• Outputs: Drought Indices — default is CDI — as a time-series dataset output in a choice of download able formats: CSV, GeoJSON (default), CoverageJSON and NetCDF for point data and then COG for areas (to be developed).

• What other component(s) can interact with the component: a desire to link to visualization/DRI analysis components. A QGIS plugin called WPS Client has been updated to be able to perform a request and view the outputted JSON file, with and a second QGIS plugin called Data Ploty used to visualize the time-series data. The Web Processing Service (WPS) link to access the drought indicator is: https://api.pixalytics.com/climate/wps?request=GetCapabilities=wps

An example Python query for a location in Canada (Latitude: 55.5 N Longitude: 99.1 W) for the SPI time series, with data for these dates/this location already cached, so runs quicker:

from owslib.wps import WebProcessingService, monitorExecution

	# contact the WPS client
    wps = WebProcessingService("http://api.pixalytics.com/climate/wps", skip_caps=True)

    # GetCapabilities
    wps.getcapabilities()

	# Execute
    inputs = [ ("start_date", '20200101'),
            ("end_date", '20221231'),
            ("latitude", '55.5'),
            ("longitude", '-99.1')]

    execution = wps.execute("drought", inputs, "output")

    outfile = "temp.json"
    monitorExecution(execution,download=True,filepath=outfile)

    # Wait 5 seconds and check
    execution.checkStatus(sleepSecs=5)

    # show status
    print('Percent complete {}%, generated {}'.format(execution.percentCompleted, outfile))

	# If there's an error print the error information
    for error in execution.errors:
        print("Error: ",error.code, error.locator, error.text)

Figure 28 — Drought indicator calling code that generated the default output, which is the CDI in GeoJSON format

• What OGC standards or formats does the component use and produce: Producing data on-the-fly using the WPS, so need to pull data through preferably an API route. The speed that the input data can be made available (i.e., extracting time-series subsets) governs the speed that the drought indicator provides data. To speed this up, input data that is not changing is being cached so that it runs significantly quicker when the API is called for a second time.

Figure 29 shows an example of the output visualized within Python using Streamlit with the intermediate data (cached as NetCDF files) as input.

Figure 29 — Plot of the CDI for a point location in Canada (Latitude: 55.5 N Longitude: 99.1 W); generated using Copernicus Emergency Management Service information [2023]

5.2.1.  Data Sources

The Global Drought Observatory

The Global Drought Observatory (GDO), owned by the Copernicus Emergency Management Services, provides a global map of coarsely-gridded agricultural drought risk, along with a breakdown of the risk for each country. The drought risk is computed using the CDI, with the variables used to compute it and other drought-related variables provided in the user portal for download, but the CDI itself is not available for download and so is being calculated in the DSW.

Figure 30 — Global Drought Observatory Web Portal, https://edo.jrc.ec.europa.eu/gdo/php/index.php?id=2001

We obtain SMA and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) from the GDO data download service. These are provided as netCDF files and contain pre-computed anomalies, so can be assimilated directly into the back-end. The SMA uses a combination of the root soil moisture from the LISFLOOD model, the MODIS land surface temperature and the ESA Climate Change Initiative (CCI) skin soil moisture [Cammalleri 2016], and the FAPAR is from NASA optical imagery.

ERA5 Reanalysis from ECMWF CDS

The CDS portal provides an API interface to return either hourly or monthly averages of the ERA5 variables. Requesting the hourly data is necessary to compute anything which requires a frequency greater than monthly, which is the case for most drought indicators (e.g. SMA) which are in dekads. To ensure there is no anti-aliasing, the full 24hr dataset for each day of the month must be downloaded. This is very time-consuming and requests will fail if the number of data points exceeds the limit, which will occur for a period of 2 years or more, even for a single location.

There is a separate application, which can also be accessed via API, to return daily data. The CDS employs a queue management system, which determines the priority of each request based partially on the computational demand of the request. The daily data retrieval relies upon an underlying service to compute the daily statistics from the hourly data, demanding more resources than simply extracting the hourly or monthly data which are pre-computed. This means the request is held in the queue for a long time (up to hours), so there is no time benefit over using the hourly data. However, for a longer time-period which would be rejected if requested hourly, this provides a workaround. A further benefit of requesting daily, rather than hourly, data is that the downloaded file is smaller.

We compute SPI and SMA using variables from the CDS API. The SPI is computed from the total precipitation in monthly intervals. The SMA is computed from the soil water volume, which is available for 4 depth levels. The SMA for each depth is computed by calculating the z-score against a long term mean, using the same baseline time period as the SPI. The most relevant depth layer can then be selected by the user; for instance, a user interested in the health of crops with shallow roots may wish to access the surfacemost layer.

ERA5 Reanalysis from AWS

Input precipitation data was also tested using ERA5 data held within the Registry of Open Data on AWS versus the CDS API and found the Amazon Web Service (AWS) Simple Storage Service (S3) stored data could be accessed faster once virtual Zarrs has been setup, but there is a question over provenance as the data on AWS was put there by an organization other than the data originator and the Zarr approach didn’t work for more recent years as the S3 stored NetCDFs have chunking that is inconsistent. An issue was raised for the Python kerchunck library, to be able to cope with variable chunking, as this isn’t current supported. The issue has also been raised with the organisation storing the data on S3, and they are investigating.

NOAA API

The NOAA Climate Environmental Data Retrieval (EDR)

Safe Software extraction of climate forecast data

We reviewed the GeoJSON Feature point data extracted from the Climate scenario RPC4.5 downscaled for Canada provided by Safe Software. As a preliminary test of combining the reanalysis and forecast data the SPI was calculated using reanalysis data up until the end of 2022 and then forecast data for 2023 and 2024; see comparisons in Figure 31.

Figure 31 — Plot of the ECMWF precipitation and SPI, extended using the Canadian climate forecast data, compared to the GDO calculated SPI; generated using Copernicus Emergency Management Service information, Copernicus Climate Service and Canadian Centre for Climate Services data [2023]

5.3.  From Raw Data to ARD with the FME Platform

5.3.1.  Component Descriptions

D100 — Client instance: Analysis Ready Data Component

Our Analysis Ready Data component (ARD) uses the FME platform to consume regional climate model and EO data and generate FAIR datasets for downstream analysis and decision support.

The challenge to manage and mitigate the effects of climate change poses difficulties for spatial and temporal data integration. One of the biggest gaps to date has been the challenge of translating the outputs of global climate models into specific impacts at the local level. FME is ideally suited to help explore options for bridging this gap given its ability to read datasets produced by climate models such as NetCDF or OGC WCS and then filter, aggregate, interpolate and restructure it as needed. FME can inter-relate it with higher resolution local data, and then output it to whatever format or service is most appropriate for a given application domain or user community.

Our ARD component supports the consumption of climate model outputs such as NetCDF. It also has the capacity to consume earth observation (EO) data, and the base map datasets necessary for downstream workflows, though given time and resource constraints during this phase we did not pursue consumption of other data types besides climate data.

5.3.1.1.  ARD Workflow

The basic workflow for generating output from the FME ARD component is as follows. The component extracts, filters, interrelates and refines these datasets according to indicator requirements. After extraction, datasets are filtered by location and transformed to an appropriate resolution and CRS. Then the workflow resamples, simplifies and reprojects the data, and then defines record level feature identifiers, ECV values, metadata and other properties to satisfy the target ARD requirements. This workflow is somewhat similar to what was needed to evaluate disaster impacts in DP21. Time ranges for climate scenarios are significantly longer — years rather than weeks for floods.

Once the climate model, and other supporting datasets have been adequately extracted, prepared and integrated, the final step is to generate the data streams and datasets required by downstream components and clients. The FME platform is well suited to deliver data in formats as needed. This includes Geopackage format for offline use. For online access, other open standards data streams are available, such as GeoJSON, KML or GML, via WFS and OGC Features APIs and other open APIs. For this pilot we generated OGC Geopackage, GeoJSON, CSV and OGC Features API services.

Figure 32 — High level FME ARD workflow showing generation of climate scenario ARD and impacts from climate model, EO, IoT, infrastructure and base map inputs

As our understanding of end user requirements continues to evolve, this will necessitate changes in which data sources are selected and how they are refined, using a model based rapid prototyping approach. We anticipate that any operational system will need to support a growing range of climate change impacts and related domains. Tools and processes must be able to absorb and integrate new datasets into existing workflows with relative ease. As the pilot develops, data volumes increase, requiring scalability methods to maintain performance and avoid overloading downstream components. Cloud based processing near cloud data sources using OGC API web services supports data scaling. Regarding the FME platform, this involves deployment of FME workflows to FME Cloud. Note that in future phases, we are likely to test how cloud native datasets (COG, STAC, ZARR) and caching can be used to scale performance as data transactions and volume requirements increase.

It is worth underlining that our ARD component depends on the appropriate data sources in order to produce the appropriate decision ready data (DRI) for downstream components. Risk factors include being able to locate and access suitable climate models of sufficient quality, resolution and timeliness to support indicators as the requirements and business rules associated with them evolve. Any data gaps encountered are documented under this section under Challenges and Opportunities and in the common Lessons Learned chapter and the end of the ER.

Figure 33 — Environment Canada NetCDF GCM time series downscaled to Vancouver area. From: https://climate-change.canada.ca/climate-data/#/downscaled-data

Figure 34 — Data Cube to ARD: NetCDF to KML, Geopackage, GeoTIFF

Original Data workflow: - Split data cube - Set timestep parameters - Compute timestep stats by band - Compute time range stats by cell - Classify by cell value range - Convert grids to vector contours

Figure 35 — Extracted timestep grids: Monthly timesteps, period mean T, period max T

Figure 36 — Convert raster temperature grids into temperature contour areas by class

Figure 37 — Geopackage Vector Area Time Series: Max Yearly Temp

5.3.1.2.  ARD Development Observations

Figure 38 — FME Data Inspector: RCM NetCDF data cube for Manitoba temperature 2020-2099

Disaster Pilot 2021 laid a good foundation for exploring data cube extraction and conversion to ARD with using the FME data integration platform. A variety of approaches were explored for extraction, simplification and transformation including approaches to select, split, aggregate, and summarize time series. However, more experimentation was needed to generate ARD that can be queried to answer questions about climate trends. This evolution of ARD was one of the goals for this CRP. This goal includes better support for both basic queries, and analytics, statistical methods, simplification & publication methods, including cloud native — NetCDF to Geopackage, GeoJSON and OGC, APIs.

In consultation with other participants, we learned fairly early on in the pilot that our approach to temperature and precipitation contours or polygons inherited from our work in DP21 on flood contours involved too much data simplification to be useful. For example, contouring required grid classification into segments, such as 5 degree C or 10mm of precipitation etc. However, this effective loss of detail oversimplified the data to the point where it no longer held enough variation over local areas to be useful. In discussion with other participants, it was determined that simply converting multidimensional data cubes to vector time series point data served the purpose of simplifying the data structure for ease of access, but retained the ECV precision needed to support a wider range of data interpretations for indicator derivation. It also meant that as a data provider we did not need to anticipate or encode interpretation of indicator business rules into our data simplification process. By simply providing ECV data points, the end user was free to run queries to find locations and time steps where temp > or precipitation < some threshold of interest.

Initially it was thought that classification rules need to more closely model impacts of interest. For example, the business rules for a heat wave might use a temperature range and stat type as part of the classification process before conversion to vector. However, this imposes the burden of domain knowledge on the data provider rather than on the climate service end user who is much more likely to understand the domain they wish to apply the data to and how best to interpret it.

Modified ARD Data workflow: - Split data cube - Set timestep parameters - Compute timestep stats by band - Compute time range stats by cell - Convert grids to vector contours

Further scenario tests were explored, including comparison with historical norms. Calculations were made using the difference between projected climate variables and historical climate variables. These climate variable deltas may well serve as a useful starting point for climate change risk indicator development. They also serve as an approach for normalizing climate impacts when the absolute units are not the main focus. Interesting patterns emerged for the LA area that we ran this process on deltas between projected and historical precipitation. While summers are typically dry and winters are wet and prone to flash floods. Initial data exploration seemed to show an increase in drought patterns in the spring and fall. More analysis needs to be done to see if this is a general pattern or simply one that emerged from the climate scenario we ran. However, this is the type of trend that local planners and managers may benefit from having the ability to explore once they have better access to climate model scenario outputs along with the ability to query and analyze them.

Figure 39 — Modified ARD Worflow: NetCDF data cube to precipitation delta grids (future - historical) in Geopackage for LA

ARD Climate Variable Delta Data workflow: - Split data cubes from historic and future netcdf inputs - Set timestep parameters - Compute historic mean for 1950 — 1980 per month based on historic time series input - Multiply historic mean by -1 - Use RasterMosaiker to sum all future grids with -1 * historic mean grid for that month - Normalize environmental variable difference by dividing by historic range for that month delta / (max — min) - Convert grids to vector contours - Define monthly environment variables from band and range values

More analysis needs to be done with higher resolution time steps — weekly and daily. At the outset monthly time steps were used to make it easier to prototype workflows. Daily time step computations will take significantly more processing time. Future pilots should explore ways of better supporting scalability of processing through automation and cloud computing approaches such as the use of cloud native formats (STAC, COG, ZARR etc).

5.3.1.3.  OGC API Features Service

Compared to OGC WFS2, OGC APIs are a simpler and more modern standard based on a REST and JSON / openAPI approach. However we found implementation of OGC API services somewhat challenging. There seems to be more complexity in terms of number of ways for requesting features, and too many options for representing service descriptions. As every client tends to interpret and use the standard a bit differently — it becomes a challenge to derive how to configure service for a wide range of clients. In particular, QGIS / ArcPro were a challenge to debug given limited logging. For QGIS, we had to examine cache files in the operating system temp directories to look for and resolve errors.

Once correctly configured, OGC API feature services seemed to perform well and likely are more efficient than the equivalent WFS2 / GML feature services. A key aspect of performance improvement was achieving query parameter continuity by passing query settings from the client all the way to the database reader configuration. For example, it was important to make sure the spatial extent and feature limits from the end user client were implemented in the database SQL extraction query and not just at an intermediate stage. We will need to explore better use of caching to further optimize performance. There may also be opportunities for pyramiding time series vector data at a lower resolution for wide area requests. This may better serve those interested in observing large area patterns who don’t necessarily need full resolution at the local level.

It should also be noted that while OGC API services should be a priority for standards support, for a climate and disaster management context, given the relative recent nature of these standards many users may be less than familiar with or prepared to use these standards. As such, there should also be provision to access data directly in well accepted open standards such as GeoJSON, CSV, GeoTIFF, Geopackage or Shape. In this project, some users preferred direct access to GeoJSON or CSV over OGC API access.

5.4.  A framework example for climate ARD generation

Wuhan University (WHU) is a university that plays a significant role in researching and teaching all aspects of surveying and mapping, remote sensing, photogrammetry, and geospatial information sciences in China. In this Climate Resilience Pilot, WHU will contribute three components (ARD, Drought Indicator, and Data Cube) and one use-case (Drought Impact Use-cases).

5.4.1.  Component: ARD

• Inputs: Gaofen L1A data and Sentinel-2 L1C data

• Outputs: Surface Reflectance ARD

• What other component(s) can interact with the component: Any components requiring access to surface reflectance data

Surface Reflectance (SR) is the fraction of incoming solar radiation reflected from the Earth’s surface for specific incidents or viewing cases. It can be used to detect the distribution and change of ground objects by leveraging the derived spectral, geometric, and textural features. Since a large amount of optical EO data has been released to the public, ARD can facilitate interoperability through time and multi-source datasets. As the probably most widely applied ARD product type, the SR ARD can contribute to climate resilience research. For example, the SR-derived NDVI series can be applied to monitor wildfire recovery by analyzing vegetation index increases. Several SR datasets have been assessed as ARD by CEOS, like the prestigious Landsat Collection 2 Level 2, and Sentinel-2 L2A, while many other datasets are still provided at a low processing level.

WHU is developing a pre-processing framework for SR ARD generation. The framework supports radiometric calibration, geometric ratification, atmospheric correction, and cloud mask. To address the inconsistencies in observations from different platforms, including variations in band settings and viewing angles, we proposed a processing chain to produce harmonized ARD. This will enable us to generate SR ARD with consistent radiometric and geometric characteristics from multi-sensor data, resulting in improved temporal coverage. In the first stage of our mission, we are focusing on the harmonization of Chinese Gaofen data and Sentinel-2 data, as shown in Figure 1, the harmonization involves spatial co-registration, band conversion, and bidirectional reflectance distribution function (BRDF) correction. Figure 2 shows the Sentinel-2 data before and after pre-processing. Furthermore, we wish to seek the assessment of CEOS-ARD in our long-term plan.

Figure 40 — The processing chain to produce harmonized ARD.

Figure 41 — Sentinel-2 RBG composite (red Band4, green Band3, blue Band2), over Hubei, acquired on October 22, 2020. (a) corresponds to the reflectance at the top of the atmosphere (L1C product); (b) corresponds to the surface reflectance after pre-processing.

5.4.2.  Component: Drought Indicator

• Inputs: Climate data, including precipitation and temperature

• Outputs: Drought risk map derived from drought indicator

• What other component(s) can interact with the component: Any components requiring access to drought risk map through OGC API

• What OGC standards or formats does the component use and produce: OGC API — Processes

Drought is a disaster whose onset, end, and extent are difficult to detect. Original meteorological data, such as precipitation, can be obtained through satellites and radar, which can be used for drought monitoring. However, the accuracy is easily affected by detection instruments and terrain occlusion, and the ability to retrieve special shapes, such as solid precipitation, is limited. In addition, many meteorological monitoring stations on the ground can provide local raw meteorological observation data. The SPEI is a model to monitor, quantitatively analyze, and determine the spatiotemporal range of the occurrence of drought using meteorological observation data from various regions. It should supplement the result of drought monitoring with satellite and radar.

SPEI has two main characteristics: 1) it considers the deficits between precipitation and evapotranspiration comprehensively, that is, the balance of water; 2) multi-time scale characteristics. For 1) drought is caused by insufficient water resources. Precipitation can increase water, while evapotranspiration can reduce water. The differences between the two variables simultaneously and in space can characterize the balance of water. For 2), the deficits value of different usable water sources is distinct at different time scales due to the different evolution cycles of different types, resulting in various representations in temporal. By accumulating the difference between precipitation and evapotranspiration at different time scales, agricultural (soil moisture) droughts, hydrological (groundwater, streamflow, and reservoir) droughts, and other droughts can be distinguished by SPEI.

In our project, the dataset for SPEI calculation is ERA5-Land monthly averaged data from 1950 to the present. We selected years of data about partial areas of East Asia for experiments. Through the following flow of the SPEI calculation, we obtain the SPEI value for assessments of drought impact. The flow of the SPEI calculation is shown in Figure 3.

Figure 42 — Flow of the SPEI calculation.

WHU has provided the SPEI drought index calculation services through the OGC API — Processes, enabling interaction with other components. The current endpoint for OGC API — Processes is http://oge.whu.edu.cn/ogcapi/processes_api. This section will explain how to use this API for calculating the drought index.

• Example：/processes http://oge.whu.edu.cn/ogcapi/processes_api/processes The API endpoint for retrieving the processes list.

• Example：/processes/{processId} http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei The API endpoint for retrieving a process description (e.g. spei). This returns the description of “spei” process, which contains the inputs and outputs information.

• Example：/processes/{processId}/execution http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei/execution The API endpoint for executing the process. The spei process exclusively supports asynchronous execution, resulting in the creation of a job for processing. The request body:

{ “inputs”: { “startTime”: “2010-01-01”, “endTime”: “2020-01-01”, “timeScale”: 5, “extent”: { “bbox”: [73.95, 17.95, 135.05, 54.05], “crs”: “http://www.opengis.net/def/crs/OGC/1.3/CRS84” } } }

• Example：/processes/{processId}/jobs/{jobId} http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei/jobs/{jobId} The API endpoint for retrieving status of a job.

• Example：/processes/{processId}/jobs/{jobId}/results http://oge.whu.edu.cn/ogcapi/processes_api/processes/spei/jobs/{jobId}/results The API endpoint for retrieving the results of a job, which are encoded as : [{ “value”: { “time”: “2000_02_01”, “url”: “http://oge.whu.edu.cn/api/oge-python/data/temp/9BC500C1B0E3438C090AF5C6F8602045/8d0357fb-8ffb-4e62-9c3a-55ad17a5831a/SPEI_2000_02_01.png” } }, …​…​ ]

Figure 43 — The SPEI results for the date 2000_02_01.

5.4.3.  Component: Data Cube

• Inputs: ERA5 temperature and precipitation data

• Outputs: Results in the form of GeoTIFF after processing in Data Cubes

• What other component(s) can interact with the component: Any components requiring access to temperature and precipitation data in part of Asia through OGC API

• What OGC standards or formats does the component use and produce: OGC API- Coverages

WHU has introduced GeoCube as a cube infrastructure for the management and large-scale analysis of multi-source data. GeoCube leverages the latest generation of OGC standard service interfaces, including OGC API-Coverages, OGC API-Features, and OGC API-Processes, to offer services encompassing data discovery, access, and processing of diverse data sources. The UML model of the GeoCube is given in Figure 5, and it has four dimensions: product, spatial, temporal, and band. Product dimension specifies the thematic axis for the geospatial data cube using the product name (e.g. ERA5_Precipitation or OSM_Water), type (e.g. raster, vector, or tabular), processes, and instrument name. For example, the product dimension can describe optical image products by recording information on the instrument and band. Spatial dimension specifies the spatial axis for the geospatial data cube using the grid code, grid type, city name, and province name. The cube uses a spatial grid for tiling to enable data readiness in a high-performance form. Temporal dimension specifies the temporal axis for the geospatial data using the phenomenon time and result time. Band dimension describes the band attribute of the raster products according to the band name, polarization mode that is reserved for SAR images, and product-level band. The product-level band is the information that is extracted from the original bands. For example, the Standardized Precipitation Evapotranspiration Index (SPEI) band is a product-level band that takes into account the hydrological process and evaluates the degree of drought by calculating the balance of precipitation and evaporation.

Figure 44 — The UML model of WHU Data Cube.

WHU has organized ERA5 temperature and precipitation data into a cube and offers climate data services through the OGC API — Coverages, supporting the computation of various climate indices. The API endpoint is http://oge.whu.edu.cn/ogcapi/coverages_api, allowing users to query and retrieve the desired data from the cube. This section provides examples demonstrating how to access the data from the cube using OGC API — Coverages.

• Example：/collections http://oge.whu.edu.cn/ogcapi/coverages_api/collections?bbox=112.65942,29.23223,115.06959,31.36234=10=2016-01-01T02:55:50Z/2018-01-01T02:55:50Z The API endpoint for querying datasets from the cube, and the query parameters including limit, bbox, and time.

• Example：/collections/{collectionId} http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602 The API endpoint for retrieving the description of the coverage with the specified ID from the cube.

• Example：/collections/{collectionId}/coverage http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602/coverage The API endpoint for retrieving the coverage in GeoTIFF format for the specified ID. Here is an example of the response:

Figure 45 — The coverage with the ID "2m_temperature_201602" in the Asian region.

• Example：/collections/{collectionId}/coverage/rangetype http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602/coverage/rangetype The API endpoint for accessing the range type of the coverage, which is part of the band dimension members in the cube. In this example, the coverage consists of only one band dimension member.

• Example：/collections/{collectionId}/coverage/domainset http://oge.whu.edu.cn/ogcapi/coverages_api/collections/2m_temperature_201602/coverage/domainset The API endpoint for the domain set of the coverage, which is also the domain set of the cube.

5.5.  ESRI Climate Resilience Data

5.5.2.  Climate Indices

To provide a more approachable context to future climate, a collection of 47 indices of future temperature and precipitation are computed. These indices build upon prior work on Climdex indices and additional indices developed for National Climate Assessment 4 (NCA4).

• Cooling Degree Days: Cooling degree days (annual cumulative number of degrees by which the daily average temperature is greater than 65°F) [degree days (degF)]

• Consecutive Dry Days: Annual maximum number of consecutive dry days (days with total precipitation less than 0.01 inches)

• Consecutive Dry Days Jan Jul Aug: Summer maximum number of consecutive dry days (days with total precipitation less than 0.01 inches in June, July, and August)

• Consecutive Wet Days: Annual maximum number of consecutive wet days (days with total precipitation greater than or equal to 0.01 inches)

• First Freeze Day: Date of the first fall freeze (annual first occurrence of a minimum temperature at or below 32degF in the fall)

• Growing Degree Days: Growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F) [degree days (degF)]

• Growing Degree Days Modified: Modified growing degree days, base 50 (annual cumulative number of degrees by which the daily average temperature is greater than 50°F; before calculating the daily average temperatures, daily maximum temperatures above 86°F and daily minimum temperatures below 50°F are set to those values) [degree days (degF)]

• Growing-season: Length of the growing (frost-free) season (the number of days between the last occurrence of a minimum temperature at or below 32degF in the spring and the first occurrence of a minimum temperature at or below 32degF in the fall)

• Growing Season 28F: Length of the growing season, 28°F threshold (the number of days between the last occurrence of a minimum temperature at or below 28°F in the spring and the first occurrence of a minimum temperature at or below 28°F in the fall)

• Growing Season 41F: Length of the growing season, 41°F threshold (the number of days between the last occurrence of a minimum temperature at or below 41°F in the spring and the first occurrence of a minimum temperature at or below 41°F in the fall)

• Heating Degree Days: Heating degree days (annual cumulative number of degrees by which the daily average temperature is less than 65°F) [degree days (degF)]

• Last Freeze Day: Date of the last spring freeze (annual last occurrence of a minimum temperature at or below 32degF in the spring)

• Precip Above 99th pctl: Annual total precipitation for all days exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]

• Precip Annual Total: Annual total precipitation [inches]

• Precip Days Above 99th pctl: Annual number of days with precipitation exceeding the 99th percentile, calculated with reference to 1976-2005 [inches]

• Precip 1in: Annual number of days with total precipitation greater than 1 inch

• Precip 2in: Annual number of days with total precipitation greater than 2 inches

• Precip 3in: Annual number of days with total precipitation greater than 3 inches

• Precip 4in: Annual number of days with total precipitation greater than 4 inches

• Precip Max 1 Day: Annual highest precipitation total for a single day [inches]

• Precip Max 5 Day: Annual highest precipitation total over a 5-day period [inches]

• Daily Avg Temperature: Daily average temperature [degF]

• Daily Max Temperature: Daily maximum temperature [degF]

• Temp Max Days Above 99th pctl: Annual number of days with maximum temperature greater than the 99th percentile, calculated with reference to 1976-2005

• Temp Max Days Below 1st pctl: Annual number of days with maximum temperature lower than the 1st percentile, calculated with reference to 1976-2005

• Days Above 100F: Annual number of days with a maximum temperature greater than 100degF

• Days Above 105F: Annual number of days with a maximum temperature greater than 105degF

• Days Above 110F: Annual number of days with a maximum temperature greater than 110degF

• Days Above 115F: Annual number of days with a maximum temperature greater than 115degF

• Temp Max 1 Day: Annual single highest maximum temperature [degF]

• Days Above 32F: Annual number of icing days (days with a maximum temperature less than 32degF)

• Temp Max 5 Day: Annual highest maximum temperature averaged over a 5-day period [degF]

• Days Above 86F: Annual number of days with a maximum temperature greater than 86degF

• Days Above 90F: Annual number of days with a maximum temperature greater than 90degF

• Days Above 95F: Annual number of days with a maximum temperature greater than 95degF

• Temp Min: Daily minimum temperature [degF]

• Temp Min Days Above 75F: Annual number of days with a minimum temperature greater than 75degF

• Temp Min Days Above 80F: Annual number of days with a minimum temperature greater than 80degF

• Temp Min Days Above 85F: Annual number of days with a minimum temperature greater than 85degF

• Temp Min Days Above 90F: Annual number of days with a minimum temperature greater than 90degF

• Temp Min Days Above 99th pctl: Annual number of days with minimum temperature greater than the 99th percentile, calculated with reference to 1976-2005

• Temp Min Days Below 1st pctl: Annual number of days with minimum temperature lower than the 1st percentile, calculated with reference to 1976-2005

• Temp Min Days Below 28F: Annual number of days with a minimum temperature less than 28degF

• Temp Min Max 5 Day: Annual highest minimum temperature averaged over a 5-day period [degF]

• Temp Min 1 Day: Annual single lowest minimum temperature [degF]

• Temp Min 32F: Annual number of frost days (days with a minimum temperature less than 32degF)

• Temp Min 5 Day: Annual lowest minimum temperature averaged over a 5-day period [degF]

The individual web services of climate indices and raster data for download can be accessed at: https://resilience.climate.gov/pages/climate-model-content-gallery

Or for each scenario:

• Historical: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-historical/about

• RCP 4.5 Early Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-4-5-early-century/about

• RCP 4.5 Mid Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-4-5-mid-century/explore?location=34.597533%2C-95.830000%2C5.00

• RCP 4.5 Late Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-4-5-late-century/about

• RCP 8.5 Early Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-8-5-early-century/about

• RCP 8.5 Mid Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-8-5-mid-century/about

• RCP 8.5 Late Century: https://resilience.climate.gov/maps/nationalclimate::u-s-climate-thresholds-loca-rcp-8-5-late-century/explore?location=34.561983%2C-95.830000%2C5.00

The data can be viewed directly in the online map viewer or opened in ArcGIS Online, ArcGIS Desktop, or a StoryMap. To view in other softwares GeoService and KMZ URLs are on the right side of the page under View API Resources.

Figure 46 — View API Resources

5.5.3.  Summarized Indices for Locations

To support easier interpretation and local decision making, the above indices were summarized by county, census tract, and tribal areas using the Zonal Statistics as Table utility in ArcGIS Pro. The results were joined into the corresponding geography polygons. A minimum, maximum, and mean value for each variable was calculated. This process was repeated for each time range and scenario. Precomputing enables quick map and graph response in the web application, and also provides as easily reusable download for someone who wants to utilize the data elsewhere.

To reuse the summarized services outside of the CRMA application or to download the processed data visit the links below for the geography of interest.

• Counties: https://resilience.climate.gov/datasets/nationalclimate::climate-mapping-resilience-and-adaptation-cmra-climate-assessment-data/explore?layer=0=0.000000%2C0.000000%2C2.74

• Census Tracts: https://resilience.climate.gov/datasets/nationalclimate::climate-mapping-resilience-and-adaptation-cmra-climate-assessment-data/explore?layer=1=-0.000000%2C0.000000%2C2.76

• American Indian/Alaska Native/Native Hawaiian Areas: https://resilience.climate.gov/datasets/nationalclimate::climate-mapping-resilience-and-adaptation-cmra-climate-assessment-data/explore?layer=2=-0.000000%2C0.000000%2C2.71

On these pages, a list of buttons allow you to filter the selection to a subset by attribute or geography, download into a variety of formats, and translate the descriptive documentation for viewing in other languages.

Figure 47 — List of buttons

6.1.  Wildfire hazard component

To develop its component, Intact migrated its previous proprietary wildfire hazard model to a private on-premise data science environment. For key inputs to the model, external connections to several open data repositories were established. To facilitate these access tests, several public open source datasets, such as climate model outputs, Earth observations, weather and geospatial, were vetted by the appropriate cybersecurity boards. The tests also informed experts of changes in platforms offerings, of new data products specifications, applicable licenses, and of current authoritative scientific references.

Figure 48 — Two samples of IFC’s current national wildfire hazard map

The table below shows the datasets accessed by Intact during the pilot.

6.1.1.  Technical Interoperability Experiments (TIE) Table

Dataset	Source	URL	Notes
National Fire Database fire polygon data	NRCan	https://cwfis.cfs.nrcan.gc.ca/datamart/download/nfdbpoly	Unable to establish SSL connection into private network
Fire Weather Index and its components	NRCan	https://cwfis.cfs.nrcan.gc.ca/downloads/fwi_obs/	Unable to establish SSL connection into private network
Forest Fuels	NRCan	ftp://ftp.nofc.cfs.nrcan.gc.ca/pub/fire/cwfis/data/fuels/
Vegetation concentration and mass	NRCan	http://tree.pfc.forestry.ca/	503 Service Unavailable from private network
Daily reanalysis composites	NOAA	https://psl.noaa.gov/data/composites/day/
Monthly reanalysis composites	NOAA	https://psl.noaa.gov/cgi-bin/data/composites/printpage.pl
Global temperature anomalies/trends	NASA	https://data.giss.nasa.gov/gistemp/maps/
Elevation at 30 meters	NASA	https://lpdaac.usgs.gov/products/nasadem_hgtv001/
Canadian Drought Monitor	AAFC	https://agriculture.canada.ca/atlas/data_donnees/canadianDroughtMonitor/data_donnees/shp/
Canadian Lightning Detection Network	NRCan	ftp://ftp.nofc.cfs.nrcan.gc.ca/pub/fire/CLDN/	Connection timed out, can’t find alternate source
Topography	USGS	https://topotools.cr.usgs.gov/gmted_viewer/viewer.htm	Interactive map, not layers
Road segments	NRCan	ftp://ftp.nofc.cfs.nrcan.gc.ca/pub/fire/cwfis/data/base_data	Connection timed out, can’t find alternate source
Population of the world	Columbia U.	https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density/data-download
CanVec Manmade Structures	NRCan	http://ftp.geogratis.gc.ca/pub/nrcan_rncan/vector/canvec/shp/ManMade/	503 Service Unavailable from private network

Below is a summarized list of the key datasets required to produce or update a wildfire hazard map.

• National fire database polygon data

• Fire Weather Index (FWI) daily maps

• Land cover maps

• Drought conditions

• Digital Elevation Model (DEM)

• Population density

• Fuel and vegetation data

Intact’s wildfire hazard map is developed exclusively for internal use. Aside from intellectual property terms, it is meant to be deployed in highly secured data environments, and as such it cannot readily interact with other components of the pilot at this point of time. The intent is to develop geospatial infrastructures and legal terms that would allow a closer collaboration with pilot’s participants.

Very early in the project, Intact also developed an H3 synthetic exposure dataset (see next figure) composed of 14M points spread out across the country in a statically representative pattern. The purpose of this dataset was to facilitate visualization and analysis of the exposure. It was also supposed to allow pilot participants to have a common exposure reference on which to develop decision-ready use cases for insurance, thus advancing towards standardization. Unfortunately, time constraints prevented update and sharing of this dataset.

Figure 49 — IFC’s exposure synthetic dataset, with Montreal – Ottawa corridor on the left, and close-up of Montreal on the right. Color scale represent relative risk density in each cell, while points are representative individual risks

6.2.  The Blue Economy

Pelagis’ participation in the Climate Resilience pilot focuses on enhancing our view of a global oceans observation system by combining real-world ground observations with analysis ready datasets. Monitoring aspects of our oceans through both a temporal and spatial continuum while providing traceability through the observations process allows stakeholders to better understand the stressors affecting the health of our oceans and investigate opportunities to mitigate the longer term implications related to climate change.

The approach to address the needs for a sustainable ocean economy is to make Marine Spatial Planning a core foundation on which to build out vertical applications. Pelagis’ platform is based on a federated information model represented as a unified social graph. This provides a decentralized approach towards designing various data streams each represented by their well-known and/or standardized model. To date, service layers based on the OGC standards for Feature, Observations & Measurements, and Sensors APIs have been developed and extended for adoption within the marine domain model. Previous work provides for data discovery and processing of features based on the IHO S-100 standard (Marine Protected Areas, Marine Traffic Management, …); NOAA open data pipelines for major weather events (Hurricane Tracking, Ocean Drifters, Saildrones …); as well as connected observation systems as provided by IOOS and its Canadian variant, CIOOS.

6.3.  ECMWF — Copernicus (will be integrated with INTRODUCTION section)

• Component: Copernicus services.

• Outputs: Copernicus Services, including Climate Data Store (CDS) https://cds.climate.copernicus.eu/ and Atmosphere Data Store (ADS) https://ads.atmosphere.copernicus.eu/.

• What other component(s) can interact with the component: CDS and ADS provide access to data via different interfaces: UI and API. It also offers a toolbox with a set of expert libraries to perform advanced operations on the available data. CDS and ADS catalogue metadata is also accessible via standard CSW. https://cds.climate.copernicus.eu/geonetwork/srv/eng/csw?SERVICE=CSW=2.0.2=GetCapabilities

• What OGC standards or formats does the component use and produce:

  • CDS and ADS catalogues exposed via CSW.

  • Access to ESGF datasets via WPS.

  • WMS is offered in some published applications.

  • CADS 2.0 (under construction) will implement OGC APIs.

6.3.1.  DRI: Heat Impact and Drought Impact Components — Safe Software

6.3.1.1.  Heat Impact DRI Component

This component takes the climate scenario summary ARD results from the ARD component and analyzes them to derive estimated heat impacts over time, based on selected climate scenarios. Central to this is the identification of key heat impact indicators required by decision makers and the business rules needed to drive them. Process steps include data aggregation and statistical analysis of maximum temperature spikes, taking into account the cumulative impacts of multiple high temperature days. Heat exhaustion effects are likely dependent on duration of heat spells, in addition to high maximum temperatures on certain days.

Figure 51 — ARD Query: Monthly Max Temp Contours

Figure 52 — ARD Query: Max Mean Monthly Temp > 25C

Figure 53 — Town of Lytton - location where entire town was devastated by fire during the heat wave of July 2021 - same location highlighted in ARD query from heat risk query in previous figure

6.3.1.2.  Drought Impact DRI Component

This component takes the climate scenario summary ARD results from the ARD component and analyzes them to derive estimated drought risk impacts over time based on selected climate scenarios. It also feeds drought related environmental factors to other pilot DRI components for more refined drought risk analysis. For the purposes of this pilot, it was recognised that more complex indicators such as drought are likely driven by multiple environmental and physical factors. As such, our initial goal was to select and provide primary climate variable data that would be useful for deriving drought risks in combination with other inputs. Given that the primary input to drought models is precipitation, or lack thereof, we developed a data flow that extracted total precipitation per month and made this available both as a time series CSV and GeoJSON datasets, as well as OGC API features time series points. This climate scenario primary drought data was provided for the province of Manitoba and for Los Angelas. These two regions were chosen since we had pilot participants interested in each of these regions and in the case of Manitoba there is also a tie in to future work as this is an area of interest for the subsequent Disaster Pilot 2023.

For the LA use case, we worked with Laubwerk to provide them with climate change impact data that could help drive a drought impact that could affect their future landscape visualization model. The idea is that based on changes to climatic variables, certain areas may be more or less suited to different vegetation types, causing the distribution of vegetation to change over time. For more on their component, please refer to section 7: From Data To Visualization.

In the case of this visualization component, simply providing precipitation totals per month were not sufficient to drive the needs of their vegetation model. In this case we did not have an intermediate drought model to feed climate variables to. In the absence of a more comprehensive drought model, we decided to develop a proxy drought risk indicator by normalizing the difference between future precipitation and past.

Calculations were made using the difference between time series grids of projected precipitation and historical grids of mean precipitation per month. These precipitation deltas were then divided by the historical max — mean per month to derive a precipitation index. The goal was to provide a value between -1 and +1 where 1 = 100% of past mean precipitation for that month. Naturally this approach can generate values that exceed the range of -1 to 1 if the projected precipitation values exceed the historic max or min. The goal was not so much to predict future absolute precipitation values but rather generate an estimated for precipitation trends given the influence of climate change. For example, this approach can help answer the question — in 30 years for a given location, compare to historical norms, by what percentage do we expect precipitation to increase or decrease. Laubwerk can then take these results and decide what degree of drought stress will cause a specific vegetation species to die out for a particular location.

Interesting patterns emerged for the LA area that we ran this process on deltas between projected and historical precipitation. While summers are typically dry and winters are wet and prone to flash floods. Initial data exploration seemed to show an increase in drought patterns in the spring and fall. More analysis needs to be done to see if this is a general pattern or simply one that emerged from the climate scenario we ran. However, this is the type of trend that local planners and managers may benefit from having the ability to explore once they have better access to climate model scenario outputs along with the ability to query and analyze them.

Figure 54 — FME Query Workflow: Geopackage precipitation delta time series to GeoJSON points

Figure 55 — FME Query Parameters: Geopackage precipitation delta time series to GeoJSON points

Figure 56 — FME Data Inspector: precipitation delta result showing potential drought risk for areas and times with significantly less precipitation than past

This approach is only a start and just scratches the surface in terms of what is possible for future drought projection based on climate model scenario ECVs. The specific business rules used to assess drought risk are still under development. FME provides a flexible data and business rule modeling framework. This means that as indicators and drought threshold rules are refined, it’s relatively straightforward to adjust the business rules in this component to refine our risk projections. Also, business rule parameters can be externalized as execution parameters so that end users can control key aspects of the scenario drought risk assessment without having to modify the published FME workflow. However one of the main goals of this pilot was not so much to produced highly refined forecast models for drought but rather to demonstrate the data value chain whereby raw climate model data cube outputs can feed a data pipeline that filters, refines, simplifies the data and ultimately can be used to drive indicators that help planners model visualize and understand the effects of climate change on the landscapes and environments within their communities.

To support future drought risk estimates for Manitoba, we also provided a precipitation forecast time series to Pixalytics as an input to their drought analytics and DRI component. Their component provides a much more sophisticated indicator of drought probability since in addition to precipitation it also takes into account soil moisture and vegetation. The goal was to extract precipitation totals per time step from the downscaled RCM — regional climate model ECV outputs for Manitoba based on CMIP5 (Coupled Model Intercomparison Project Phase 5) model results obtained from Environment Canada. For this use case the grids have a spatial resolution of roughly 10km and a temporal resolution a monthly time step. Pixalytics then ran their drought model based on these precipitation estimates in order to asses potential future drought risk in southern Manitoba. The data was provided to Pixalytics initially as a GeoJSON feed of 2d points derived from the data cube cells with precipitation totals per cell. We later also provided this same data feed as a OGC API Feature service.

For future phases of the climate or disaster pilots, it may be useful to explore additional approaches for both precipitation data analysis and combination with other related datasets and external models. It may be useful to segment cumulative rainfall below a certain threshold Pt within a certain time window (days, weeks or months), since cumulative rainfall over time will be crucial for computing water budgets by watershed or catch basin. To do this we would like to test the use of a higher resolution time step such as daily, to see if the increased resolution reveals patterns of interest that the coarser monthly time step does not. There are also other statistical RCM results that might be useful to make available (mean, min, max). Besides precipitation, climate models also generate soil moisture predictions which could used by this component to assess drought risk. This component would also benefit from integration with topography, DEMs and hydrology related data such as river networks, water bodies, aquifers and watershed boundaries. Therefore rather than just computing precipitation deltas at the cell level, it would likely be useful to sum precipitation by catch basin and compute future trends that may indicate potential drought or flood.

The specific business rules used to assess drought risk are still under development. FME provides a flexible data and business rule modeling framework. This means that as indicators and drought threshold rules are refined, it’s relatively straightforward to adjust the business rules in this component to refine our risk projections. Also, business rule parameters can be externalized as execution parameters so that end users can control key aspects of the scenario drought risk assessment without having to modify the published FME workflow.

It should be stressed that the field of drought modelling is not new and there are many drought modelling tools available that are far more sophisticated than anything described above. As such, subsequent Climate and Disaster pilots should explore how future climate projections can be funneled into these more mature climate models in an automated fashion to produce more refined estimates of projected drought risk. That said, we need to start somewhere, and it is hoped that this basic demonstration of the raw data to ARD to DRI value chain for drought can provide some insights into what type of indicators we may want to generate to help better understand future drought risks, and where we may want to improve on this process.